M4-ai (M4-ai)

posted an update about 4 hours ago

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303

Dropping Downstream tasks using newly initialized parameters and weights ([classifier.bias & weights]) support domain-specific 𝗶𝗺𝗮𝗴𝗲 𝗰𝗹𝗮𝘀𝘀𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻. Based on siglip2-base-patch16-224 and DomainNet (single-domain, multi-source adaptation), with Fashion-MNIST for experimental testing. 🧤☄️

Fashion-Mnist : prithivMLmods/Fashion-Mnist-SigLIP2
Multisource-121 : prithivMLmods/Multisource-121-DomainNet
Painting-126 : prithivMLmods/Painting-126-DomainNet
Sketch-126 : prithivMLmods/Sketch-126-DomainNet
Clipart-126 : prithivMLmods/Clipart-126-DomainNet

Models are trained with different parameter settings for experimental purposes only, with the intent of further development. Refer to the model page below for instructions on running it with Transformers 🤗.

Collection : prithivMLmods/domainnet-exp-67e0e3c934c03cc40c6c8782

Citations : SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features https://arxiv.org/pdf/2502.14786 & Moment Matching for Multi-Source Domain Adaptation : https://arxiv.org/pdf/1812.01754

prithivMLmods

posted an update 4 days ago

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2139

Play with Orpheus TTS, a Llama-based Speech-LLM designed for high-quality, empathetic text-to-speech generation. This model has been fine-tuned to deliver human-level speech synthesis 🔥🗣️

👉GitHub: https://github.com/PRITHIVSAKTHIUR/Orpheus-TTS-Edge

Demo supporting both text-to-speech and text-to-llm responses in speech.

> voice: tara, dan, emma, josh
> emotion: <laugh>, <chuckle>, <sigh>, <cough>, <sniffle>, <groan>, <yawn>, <gasp>.

🥠Orpheus-3b-0.1-ft
Model Page: canopylabs/orpheus-3b-0.1-ft

🥠Orpheus-3b-0.1-ft
Colab Inference Notebook: https://colab.research.google.com/drive/1KhXT56UePPUHhqitJNUxq63k-pQomz3N?usp=sharing

🥠Finetune [ orpheus-3b-0.1-pretrained ]
Resource: https://github.com/canopyai/Orpheus-TTS/tree/main/finetune

🥠Model-releases:
https://canopylabs.ai/model-releases

1 reply

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AtAndDev

posted an update 8 days ago

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4090

There seems to multiple paid apps shared here that are based on models on hf, but some ppl sell their wrappers as "products" and promote them here. For a long time, hf was the best and only platform to do oss model stuff but with the recent AI website builders anyone can create a product (really crappy ones btw) and try to sell it with no contribution to oss stuff. Please dont do this, or try finetuning the models you use...
Sorry for filling yall feed with this bs but yk...

6 replies

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prithivMLmods

posted an update 10 days ago

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Hey Guys! One Small Announcement 🤗
Stranger Zone now accepts LoRA requests!

✍️Request : strangerzonehf/Request-LoRA [ or ] strangerzonehf/Request-LoRA#1

Page : https://huggingface.co/strangerzonehf

Describe the artistic properties by posting sample images or links to similar images in the request discussion. If the adapters you're asking for are truly creative and safe for work, I'll train and upload the LoRA to the Stranger Zone repo!

Thank you!

AtAndDev

posted an update 12 days ago

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Gemma 3 seems to be really good at human preference. Just waiting for ppl to see it.

prithivMLmods

posted an update 12 days ago

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Gemma-3-4B : Image and Video Inference 🖼️🎥

🧤Space: prithivMLmods/Gemma-3-Multimodal
🥠Git : https://github.com/PRITHIVSAKTHIUR/Gemma-3-Multimodal

@gemma3 : {Tag + Space_+ 'prompt'}
@video-infer : {Tag + Space_+ 'prompt'}

+ Gemma3-4B : google/gemma-3-4b-it
+ By default, it runs : prithivMLmods/Qwen2-VL-OCR-2B-Instruct

Gemma 3 Technical Report : https://storage.googleapis.com/deepmind-media/gemma/Gemma3Report.pdf

1 reply

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not-lain

posted an update 12 days ago

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🚀AraClip is now fully integrated with Hugging Face 🤗

AraClip is a specialized CLIP model that was created by @pain and optimized for Arabic text-image retrieval tasks🔥

🔗 Try it out 🔗
🤖 model: Arabic-Clip/araclip
🧩 Gradio demo: Arabic-Clip/Araclip-Simplified
🌐 website: https://arabic-clip.github.io/Arabic-CLIP/

2 replies

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prithivMLmods

posted an update 13 days ago

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Variable Demo for Two Image-to-Text-to-Text Multimodals 🌠

📜Space: prithivMLmods/Multimodal-OCR

By default, it will use:
prithivMLmods/Qwen2-VL-OCR-2B-Instruct or
prithivMLmods/Qwen2-VL-OCR2-2B-Instruct

To trigger Aya-Vision's 8B by @aya-vision , use the prompt:
CohereForAI/aya-vision-8b

Tonic

posted an update 17 days ago

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1156

🙋🏻‍♂️Hey there folks,

Did you know that you can use ModernBERT to detect model hallucinations ?

Check out the Demo : Tonic/hallucination-test

See here for Medical Context Demo : MultiTransformer/tonic-discharge-guard

check out the model from KRLabs : KRLabsOrg/lettucedect-large-modernbert-en-v1

and the library they kindly open sourced for it : https://github.com/KRLabsOrg/LettuceDetect

👆🏻if you like this topic please contribute code upstream 🚀

2 replies

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Tonic

posted an update 19 days ago

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Powered by KRLabsOrg/lettucedect-large-modernbert-en-v1 from KRLabsOrg.

Detect hallucinations in answers based on context and questions using ModernBERT with 8192-token context support!

### Model Details
- **Model Name**: [lettucedect-large-modernbert-en-v1]( KRLabsOrg/lettucedect-large-modernbert-en-v1)
- **Organization**: [KRLabsOrg](https://huggingface.co/KRLabsOrg)
- **Github**: [https://github.com/KRLabsOrg/LettuceDetect](https://github.com/KRLabsOrg/LettuceDetect)
- **Architecture**: ModernBERT (Large) with extended context support up to 8192 tokens
- **Task**: Token Classification / Hallucination Detection
- **Training Dataset**: [RagTruth]( wandb/RAGTruth-processed)
- **Language**: English
- **Capabilities**: Detects hallucinated spans in answers, provides confidence scores, and calculates average confidence across detected spans.

LettuceDetect excels at processing long documents to determine if an answer aligns with the provided context, making it a powerful tool for ensuring factual accuracy.

prithivMLmods

posted an update 19 days ago

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SigLIP2 Image Classification 🧤

> https://huggingface.co/blog/prithivMLmods/siglip2-finetune-image-classification

Locutusque

posted an update 27 days ago

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🎉 Exciting news, everyone! I've just released **Thespis-Llama-3.1-8B**, a new language model designed for enhanced roleplaying! ✨️

It's built on Llama-3.1 and fine-tuned with a focus on Theory of Mind reasoning to create more believable and engaging characters. It even learned a few tricks on its own, like adding in-character thought processes! 🧠

Check it out here: Locutusque/Thespis-Llama-3.1-8B

Give it a try and let me know what you think! I'm especially interested in feedback on how well the characters stay in role and if the responses feel natural. Looking forward to seeing what amazing stories you create! ✍️

prithivMLmods

posted an update 27 days ago

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Dropping some of the custom fine-tunes based on SigLIP2,
with a single/multi label classification problem type! 🌀🧤

- AI vs Deepfake vs Real : prithivMLmods/AI-vs-Deepfake-vs-Real-Siglip2
- Deepfake Detect : prithivMLmods/Deepfake-Detect-Siglip2
- Fire Detection : prithivMLmods/Fire-Detection-Siglip2
- Deepfake Quality Assess : prithivMLmods/Deepfake-Quality-Assess-Siglip2
- Guard Against Unsafe Content : prithivMLmods/Guard-Against-Unsafe-Content-Siglip2

🌠Collection : prithivMLmods/siglip2-custom-67bcdb2de8fe96b99fb4e19e

KnutJaegersberg

posted an update about 1 month ago

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1169

Designing Ecosystems of Intelligence from First Principles

(new major Friston paper)

https://arxiv.org/abs/2212.01354

prithivMLmods

posted an update about 1 month ago

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It's really interesting about the deployment of a new state of matter in Majorana 1: the world’s first quantum processor powered by topological qubits. If you missed this news this week, here are some links for you:

🅱️Topological qubit arrays: https://arxiv.org/pdf/2502.12252

⚛️ Quantum Blog: https://azure.microsoft.com/en-us/blog/quantum/2025/02/19/microsoft-unveils-majorana-1-the-worlds-first-quantum-processor-powered-by-topological-qubits/

📖 Read the story: https://news.microsoft.com/source/features/innovation/microsofts-majorana-1-chip-carves-new-path-for-quantum-computing/

📝 Majorana 1 Intro: https://youtu.be/Q4xCR20Dh1E?si=Z51DbEYnZFp_88Xp

🌀The Path to a Million Qubits: https://youtu.be/wSHmygPQukQ?si=TS80EhI62oWiMSHK

3 replies

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mmhamdy

posted an update about 1 month ago

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2743

🎉 We're excited to introduce MemoryCode, a novel synthetic dataset designed to rigorously evaluate LLMs' ability to track and execute coding instructions across multiple sessions. MemoryCode simulates realistic workplace scenarios where a mentee (the LLM) receives coding instructions from a mentor amidst a stream of both relevant and irrelevant information.

💡 But what makes MemoryCode unique?! The combination of the following:

✅ Multi-Session Dialogue Histories: MemoryCode consists of chronological sequences of dialogues between a mentor and a mentee, mirroring real-world interactions between coworkers.

✅ Interspersed Irrelevant Information: Critical instructions are deliberately interspersed with unrelated content, replicating the information overload common in office environments.

✅ Instruction Updates: Coding rules and conventions can be updated multiple times throughout the dialogue history, requiring LLMs to track and apply the most recent information.

✅ Prospective Memory: Unlike previous datasets that cue information retrieval, MemoryCode requires LLMs to spontaneously recall and apply relevant instructions without explicit prompts.

✅ Practical Task Execution: LLMs are evaluated on their ability to use the retrieved information to perform practical coding tasks, bridging the gap between information recall and real-world application.

📌 Our Findings

1️⃣ While even small models can handle isolated coding instructions, the performance of top-tier models like GPT-4o dramatically deteriorates when instructions are spread across multiple sessions.

2️⃣ This performance drop isn't simply due to the length of the context. Our analysis indicates that LLMs struggle to reason compositionally over sequences of instructions and updates. They have difficulty keeping track of which instructions are current and how to apply them.

🔗 Paper: From Tools to Teammates: Evaluating LLMs in Multi-Session Coding Interactions (2502.13791)
📦 Code: https://github.com/for-ai/MemoryCode

mmhamdy

authored 2 papers about 1 month ago

MMTEB: Massive Multilingual Text Embedding Benchmark

Paper • 2502.13595 • Published Feb 19 • 33

From Tools to Teammates: Evaluating LLMs in Multi-Session Coding Interactions

Paper • 2502.13791 • Published Feb 19 • 5

KnutJaegersberg

posted an update about 1 month ago

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719

Mimicking Consciousness in LLMs: Ascending the Dimensions of Thought with Recurrent Processing

This blog post explores how **recurrent processing** can transform Large Language Models (LLMs) to mimic aspects of human thought by engaging in iterative feedback loops. Inspired by string theory, the post describes how LLMs can "ascend dimensions" of cognition, progressing through foundational cognitive loops—such as basic cognition, executive functions, and meta-cognition—before advancing into **world simulation**. In this stage, LLMs explore higher dimensions, perceiving non-linear time, simulating branching possibilities, and integrating multiple realities. The interaction between the **Generator** and **Reflective Compass** allows AI systems to refine their outputs iteratively, moving toward a **point attractor** where ideas become coherent and polished. While this process doesn't bestow true consciousness, it offers a compelling imitation of reflective and adaptive thinking, leading to smarter dialogue, enhanced creativity, and more robust problem-solving.

https://huggingface.co/blog/KnutJaegersberg/oscillatory-recurrence-for-llms

prithivMLmods

posted an update about 1 month ago

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3928

Dino: The Minimalist Multipurpose Chat System 🌠
Agent-Dino : prithivMLmods/Agent-Dino
Github: https://github.com/PRITHIVSAKTHIUR/Agent-Dino

By default, it performs the following tasks:
{Text-to-Text Generation}, {Image-Text-Text Generation}
@image: Generates an image using Stable Diffusion xL.
@3d: Generates a 3D mesh.
@web: Web search agents.
@rAgent: Initiates a reasoning chain using Llama mode for coding explanations.
@tts1-♀, @tts2-♂: Voice generation (Female and Male voices).
@yolo : Object Detection

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MMTEB: Massive Multilingual Text Embedding Benchmark

From Tools to Teammates: Evaluating LLMs in Multi-Session Coding Interactions

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